Sleep is a physiological process that is commonly described in sleep stages. For example, sleep stages may be classified in: wake, light, deep and rapid eye movement (REM) sleep. The sleep stage may be deduced from measurements of brain activity and transition between these stages. Sleep stages can be identified by labeling brain oscillations captured with proper equipment that logs signals from electrodes attached to the skull of a subject. This way of annotating sleep stages is reliable and considered a “golden standard” in the field of sleep medicine.
An automated detection that a human sleeps, and in particular, automated classification of the particular sleep stage he/she is in, has many applications. For sleep monitoring, in particular, ambulatory and/or unobtrusive sleep monitoring, in particular, home sleep monitoring, the use of an electroencephalogram (EEG) sensor configured to monitor electrical activity of the brain of the human which would be needed to detect sleep from brain activity is considered disadvantageous. An EEG sensor is worn on the head, e.g., in the form of a head cap or head band, and obstructs natural sleep.
Detecting sleep and/or sleep stages could be done using sensors other than the EEG sensors, e.g., using a sensor configured to monitor a bodily function of the human. Such sensors are potentially more comfortable for the user. For example, a non-contact sensor, that detects a bodily function without being in contact with the user disturbs sleep only slightly and is therefore comfortable. If a contact sensor is not wanted or is not possible, it is preferred to use a sensor that is not connected to the head and/or not connected to another device using wires, e.g., a wireless actiometer configured for wearing around a wrist or ankle.
However, sleep classification from a sensor configured to monitor a bodily function of the human lacks accuracy if that sensor is not an EEG sensor configured to monitor electrical activity of the brain worn in direct contact or close proximity to the upper part of the human head covering the brain.
US 20080157956A1 discloses a method where sleep sensor signals are obtained to a mobile communication device from sensor devices. The mobile communication device checks the sleep sensor signals for a sleep state transition, determines the type of the sleep state transition, forms control signals based on the type of the sleep state transition and sends the control signals to at least one electronic device.
U.S. Pat. No. 8,021,299B2 discloses to correlate values of a non-polysomnographic (non-PSG) physiological parameter set to polysomnographically (PSG) determined sleep states. The correlated values of the non-PSG parameter set and sleep states may be analyzed, and a relationship between the values and sleep states may be determined. The relationship may allow determination of sleep states for any given patient based on values of the non-PSG physiological parameter set for the patient. The non-PSG physiological parameter set does not include physiological parameters typically required for PSG, such as brain electrical activity (EEG), eye movement (EOG), and jaw or neck muscular activity or tone (EMG); Medical devices, such as implantable medical devices (IMDs) that would generally be unable to monitor such physiological parameters, may apply the relationship to values of the non-PSG physiological parameter set for a patient to identify sleep states of the patient.
US 20110230790A1 discloses a method for operating a sleep phase actigraphy synchronized alarm clock that communicates with a remote sleep database, such as an internet server database, and compares user physiological parameters, sleep settings, and actigraphy data with a large database that may include data collected from a large number of other users with similar physiological parameters, sleep settings, and actigraphy data. The remote server may use “black box” analysis approach by running supervised learning algorithms to analyze the database, producing sleep phase correction data which can be uploaded to the alarm clock, and be used by the alarm clock to further improve its REM sleep phase prediction accuracy.